Framing Islamophobia in International Media: An Analysis of Terror Attacks against Muslims and Non-Muslims
Bibliographic record
Abstract
The study is focused to analyze the framing of the islamophobia in the international media in context of the terror attacks on the Muslims and Non-Muslims where the newspapers from six countries including United States, India, United Kingdom, Canada, Australia and Pakistan are focused to study to analyze the major terror incidents from 2014 to 2019 in different countries of the world. The key focus was to analyze the frames including perpetrator of the terror incidents; Islam/Muslims are Progressive or Violent; Criticism on Muslims and Non-Muslims Perpetrators; Target are Muslims or Non-Muslims and Positive or Negative image of Islam/Muslims presented. The content analysis method is used to analyze the content about framing of the major terror incidents targeting both the Muslims and Non-Muslims. The study concludes that the selected international press presented Islam in context of anti-Muslim wave as they presented Islam and Muslims in a negative context mostly linking them with violence and non-Muslims are more target of terrorism than the Muslims. The study presents that the Muslims and Islam is targeted more despite the fact that they have also been target of the terrorism and extremism losing hundreds of lives. Only Pakistani newspaper presented a positive image of Islam and the Muslims convincing about the fact that Muslims are equal target of terrorism and extremism and Muslims also have suffered by terrorism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".